Violin Plots show the distribution of a data set by displaying the mean, median, and curve of the data. It is easy to see where the bulk of the data is by the thining and widening of the graph.
Here, the bulk of the data is at the lower level degrees because the graph is at its widest at that point.
Histograms summarize data distribution by showing how frequently data occurs within certain intervals. The height of the bar gives the frequency in the respective interval. Here, it is also clear that the bulk of the data is at the lower level degrees because they have the higher bars.
Kernel Density Plots show where the values of a data set are contained. This is done by the rising and falling peaks, similar to the histogram. Again, here the bulk of the data appears to be at the lower level degrees because they have the higher peaks.
Good execution of the graphs, but I wonder how appropriate they are... The difficulty is that this is a factor variable; it's just how many people are in each category of education. So the visualizations you've chosen are not the best. In fact, I don't believe the categories are ordered from lowest to highest education...are they? In other words, what does 1,2,3,4,5,6,7,8,9,10,11 mean in the second graph? That's something you have to know and think through! I believe the best way to represent this variable is with a bar chart of several bars, each representing the number of each people in each category.
Violin Plots show the distribution of a data set by displaying the mean, median, and curve of the data. It is easy to see where the bulk of the data is by the thining and widening of the graph.
Here, the bulk of the data is at the 40-60 response to the feeling thermometer because the graph is at its widest at that point.
Histograms summarize data distribution by showing how frequently data occurs within certain intervals. The height of the bar gives the frequency in the respective interval. Here, it is also clear that the bulk of the data is at the 40-60 response to the feeling thermometer because that interval has the highest bars.
Kernal Density Plots show where the values of a data set are contained. This is done by the rising and falling peaks, similar to the histogram. Again, here the bulk of the data appears to be at the 40-60 response to the feeling thermometer because that interval has the highest peaks.
Now that you're looking at a continuous, quantitative variable, these charts are great and definitely appropriate.
Scatter plots display values for two variables for a set of data. They are used to compare the relationship between the two variables. In this case, I compared the feeling thermometers
of the Working Class to People on Welfare. It does not appear that there is a definite correlation because the coordinates are not sloping in a particular pattern.
These scatterplots with so many data points are hard to interpret, and actually scatterplots may not even be the best tool. But that's my fault, because that's what you've learned so far! However, you also learned to add a line of best fit, which really helps us see any tendency that might be in the data. That would really really help here!
Violin Plots show the distribution of a data set by displaying the mean, median, and curve of the data. It is easy to see where the bulk of the data is by the thining and widening of the graph.
Here, the bulk of the data is at the lower level degrees because the graph is at its widest at that point.
Histograms summarize data distribution by showing how frequently data occurs within certain intervals. The height of the bar gives the frequency in the respective interval. Here, it is also clear that the bulk of the data is at the lower level degrees because they have the higher bars.
Kernel Density Plots show where the values of a data set are contained. This is done by the rising and falling peaks, similar to the histogram. Again, here the bulk of the data appears to be at the lower level degrees because they have the higher peaks.
Good execution of the graphs, but I wonder how appropriate they are... The difficulty is that this is a factor variable; it's just how many people are in each category of education. So the visualizations you've chosen are not the best. In fact, I don't believe the categories are ordered from lowest to highest education...are they? In other words, what does 1,2,3,4,5,6,7,8,9,10,11 mean in the second graph? That's something you have to know and think through! I believe the best way to represent this variable is with a bar chart of several bars, each representing the number of each people in each category.
Violin Plots show the distribution of a data set by displaying the mean, median, and curve of the data. It is easy to see where the bulk of the data is by the thining and widening of the graph.
Here, the bulk of the data is at the 40-60 response to the feeling thermometer because the graph is at its widest at that point.
Histograms summarize data distribution by showing how frequently data occurs within certain intervals. The height of the bar gives the frequency in the respective interval. Here, it is also clear that the bulk of the data is at the 40-60 response to the feeling thermometer because that interval has the highest bars.
Kernal Density Plots show where the values of a data set are contained. This is done by the rising and falling peaks, similar to the histogram. Again, here the bulk of the data appears to be at the 40-60 response to the feeling thermometer because that interval has the highest peaks.
Now that you're looking at a continuous, quantitative variable, these charts are great and definitely appropriate.
Scatter plots display values for two variables for a set of data. They are used to compare the relationship between the two variables. In this case, I compared the feeling thermometers
of the Working Class to People on Welfare. It does not appear that there is a definite correlation because the coordinates are not sloping in a particular pattern.
These scatterplots with so many data points are hard to interpret, and actually scatterplots may not even be the best tool. But that's my fault, because that's what you've learned so far! However, you also learned to add a line of best fit, which really helps us see any tendency that might be in the data. That would really really help here!